Effect of a mobile phone-based interactive voice response on common childhood illnesses in Ghana: a quasi-experimental study
Bibliographic record
Abstract
BACKGROUND: Malaria, acute respiratory infections (ARIs), and diarrhoea are primary causes of morbidity and mortality among children under five years old in Ghana. Despite the implementation of various interventions, the nation struggles to meet relevant health and policy targets. While the potential of mobile health interventions to enhance child health outcomes has been recognized, their impact on prevalent childhood illnesses remains insufficiently explored. This implementation research study aimed to evaluate the effect of a mobile health information system (mHIS) intervention on common childhood illnesses among under-five children residing in rural health districts of Ghana. METHODS: In this quasi-experimental study, we enrolled all children under five years old from randomly selected clusters within the rural intervention and control health districts in the Ashanti region, Ghana between November 2018 and December 2021. The Reach, Effectiveness, Adoption Implementation and Maintenance (RE-AIM) framework was used to design and implement the intervention. The intervention involved a mobile phone-based information system to monitor childhood conditions, offer telemedicine consultations, and deliver child health promotion messages on nutrition and management of common childhood illnesses to caregivers. By employing the average treatment effect (ATET) and difference-in-difference (DiD) analyses, we assessed outcome disparities in diarrhoea, cough, and presumptive malaria. RESULTS: The incidence of diarrhoea and malaria decreased in the intervention group. The ATET analysis indicated pre-intervention disparities in presumptive malaria with a post-intervention difference between the groups for diarrhoea and presumptive malaria. Results related to cough, used as a proxy for ARIs, did not provide conclusive results across the intervention and control sites based on this intervention. However, the DiD model highlighted an overall statistically significant reduction in diarrhoea and presumptive malaria. CONCLUSION: This study underscores the effectiveness of a mobile phone-based health information system intervention in curbing common childhood morbidities, particularly diarrhoea and presumptive malaria, among under-five children in rural Ghana. This approach demonstrates promise in advancing child health outcomes and contributing to the reduction of prevalent illnesses in resource-constrained settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".